Comparison of Pulmonary Maturation Differences Among Black and White Infants
Bibliographic record
Abstract
BACKGROUND: Bias exists that infants of color have better outcomes in the neonatal intensive care unit compared to White infants. These presumptions stem from perceived differences in pulmonary maturation between Black and White infants. PURPOSE: To compare the incidence of respiratory morbidity in Black and White infants, and to identify if pulmonary maturation differences exist. DATA SOURCES: Databases included MEDLINE (Ovid), Embase (Elsevier), and Web of Science (Clarivate). STUDY SELECTION: All identified studies were uploaded into Covidence. A total of 2124 citations were screened in the abstract phase. Study selection was carried out independently by 2 authors and excluded if did not meet inclusion criteria. Disagreements were resolved by adjudication by third reviewer. Article selection presented by flowchart as per PRISMA guidelines. DATA EXTRACTION: A citation tracking system was used to identify relevant studies included in the full text review. RESULTS: Though differences among Black and White infants were present, it was not found that race alone had a causal impact on an infant's pulmonary maturation, but rather that these differences in outcomes could be related to health disparities impacted by race. IMPLICATIONS FOR PRACTICE AND RESEARCH: As providers driving care and making treatment decisions for neonatal patients, we must be aware of our implicit biases regarding neonatal lung development. Additional research is essential to drive policy change and ensure equitable healthcare and reduce infant mortality and morbidity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".